An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. An autonomous vehicle senses its environment using sensing devices such as radar, lidar, image sensors, and the like. The autonomous vehicle system further uses information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.
Vehicle automation has been categorized into numerical levels ranging from Zero, corresponding to no automation with full human control, to Five, corresponding to full automation with no human control. Various automated driver-assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems correspond to lower automation levels, while true “driverless” vehicles correspond to higher automation levels.
To achieve high level automation, vehicles are often equipped with an increasing number of different types of devices for analyzing the environment around the vehicle, such as, for example, cameras or other imaging devices capturing imagery of the environment, radar or other ranging devices for surveying or detecting features within the environment, and the like. In practice, the different onboard devices are located at different locations onboard the vehicle and typically operate at different sampling rates or refresh rates, and as a result, capture different types of data corresponding to different points in time from different viewpoints or perspectives. Calibrating relationships between different devices improves the ability to accurately establish correlations between different types of data, which, in turn facilitate assigning attributes to objects or features within the environment more accurately, thereby improving autonomous vehicle controls.
Focusing or tuning the analysis on particular regions of sensor data can improve the performance of the analysis. Additionally, limiting the amount of sensor data under consideration can reduce the overall amount of computational resources and time associated with performing object analysis on the sensor data. Accordingly, it is desirable to delineate and partition regions of sensor data to reduce the amount of resources that could otherwise be devoted towards analysis of data unlikely to yield relevant or significant results. Other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.